Negative sampling. We find that a good negative sampling strategy is essential for the model's convergence. Existing approaches either use all but \(\mathbf {x}_t\)  (positive) within a mini-batch as negative samples or limit it to the current sequence only. However, both these methods ignore the data content and thus can miss useful negatives. [1]} showed that leveraging prior knowledge about data can improve the negative sample quality (e.g., by sampling negatives from the same speaker as the positive). Unfortunately, such prior knowledge is often not available in unlabeled videos.
